SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Showing 291300 of 3073 papers

TitleStatusHype
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
Active Sensing for Communications by LearningCode1
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
Active Statistical InferenceCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Active Testing: Sample-Efficient Model EvaluationCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Evidential Uncertainty Quantification: A Variance-Based PerspectiveCode1
Active Anomaly Detection via EnsemblesCode1
Fluent: An AI Augmented Writing Tool for People who StutterCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified